// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This is an example illustrating the use of the Bayesian Network
inference utilities found in the dlib C++ library. In this example
we load a saved Bayesian Network from disk.
*/#include<dlib/bayes_utils.h>#include<dlib/graph_utils.h>#include<dlib/graph.h>#include<dlib/directed_graph.h>#include<iostream>#include<fstream>usingnamespace dlib;
usingnamespace std;
// ----------------------------------------------------------------------------------------
intmain(int argc, char** argv){try{// This statement declares a bayesian network called bn. Note that a bayesian network
// in the dlib world is just a directed_graph object that contains a special kind
// of node called a bayes_node.
directed_graph<bayes_node>::kernel_1a_c bn;
if(argc !=2){
cout << "You must supply a file name on the command line. The file should "
<< "contain a serialized Bayesian Network" << endl;
return1;
}
ifstream fin(argv[1],ios::binary);
// Note that the saved networks produced by the bayes_net_gui_ex.cpp example can be deserialized
// into a network. So you can make your networks using that GUI if you like.
cout << "Loading the network from disk..." << endl;
deserialize(bn, fin);
cout << "Number of nodes in the network: " << bn.number_of_nodes()<< endl;
// Let's compute some probability values using the loaded network using the join tree (aka. Junction
// Tree) algorithm.
// First we need to create an undirected graph which contains set objects at each node and
// edge. This long declaration does the trick.
typedef graph<dlib::set<unsignedlong>::compare_1b_c, dlib::set<unsignedlong>::compare_1b_c>::kernel_1a_c join_tree_type;
join_tree_type join_tree;
// Now we need to populate the join_tree with data from our bayesian network. The next two
// function calls do this. Explaining exactly what they do is outside the scope of this
// example. Just think of them as filling join_tree with information that is useful
// later on for dealing with our bayesian network.
create_moral_graph(bn, join_tree);
create_join_tree(join_tree, join_tree);
// Now we have a proper join_tree we can use it to obtain a solution to our
// bayesian network. Doing this is as simple as declaring an instance of
// the bayesian_network_join_tree object as follows:
bayesian_network_join_tree solution(bn, join_tree);
// now print out the probabilities for each node
cout << "Using the join tree algorithm:\n";
for(unsignedlong i =0; i < bn.number_of_nodes(); ++i){// print out the probability distribution for node i.
cout << "p(node " << i <<") = " << solution.probability(i);
}}catch(exception& e){
cout << "exception thrown: " << e.what()<< endl;
return1;
}}